Hardness of Submodular Cost Allocation: Lattice Matching and a Simplex Coloring Conjecture

Authors Alina Ene, Jan Vondrák



PDF
Thumbnail PDF

File

LIPIcs.APPROX-RANDOM.2014.144.pdf
  • Filesize: 0.52 MB
  • 16 pages

Document Identifiers

Author Details

Alina Ene
Jan Vondrák

Cite As Get BibTex

Alina Ene and Jan Vondrák. Hardness of Submodular Cost Allocation: Lattice Matching and a Simplex Coloring Conjecture. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 28, pp. 144-159, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014) https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2014.144

Abstract

We consider the Minimum Submodular Cost Allocation (MSCA) problem.
In this problem, we are given k submodular cost functions f_1, ... ,
f_k: 2^V -> R_+ and the goal is to partition V into k sets A_1, ...,
A_k so as to minimize the total cost sum_{i = 1}^k f_i(A_i). We show
that MSCA is inapproximable within any multiplicative factor even in
very restricted settings; prior to our work, only Set Cover hardness
was known. In light of this negative result, we turn our attention
to special cases of the problem. We consider the setting in which
each function f_i satisfies f_i = g_i + h, where each g_i is monotone
submodular and h is (possibly non-monotone) submodular.  We give an
O(k log |V|) approximation for this problem. We provide some evidence
that a factor of k may be necessary, even in the special case of
HyperLabel. In particular, we formulate a simplex-coloring
conjecture that implies a Unique-Games-hardness of (k - 1 - epsilon)
for k-uniform HyperLabel and label set [k]. We provide a proof of the
simplex-coloring conjecture for k=3.

Subject Classification

Keywords
  • Minimum Cost Submodular Allocation
  • Submodular Optimization
  • Hypergraph Labeling

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. G. Birkhoff. Rings of sets. Duke Mathematical Journal, 3:443-454, 1937. Google Scholar
  2. C. Chekuri and A. Ene. Approximation algorithms for submodular multiway partition. In Proc. of IEEE FOCS, 2011. Google Scholar
  3. C. Chekuri and A. Ene. Submodular cost allocation problem and applications. In Proc. of ICALP, pages 354-366, 2011. Google Scholar
  4. G. Călinescu, H. J. Karloff, and Y. Rabani. Approximation algorithms for the 0-extension problem. In Proc. of ACM-SIAM SODA, 2001. Google Scholar
  5. W. H. Cunningham. On submodular function minimization. Combinatorica, 5(3):185-192, 1985. Google Scholar
  6. A. Ene. Approximation algorithms for submodular optimization and graph problems. Ph.D. thesis, University of Illinois, Urbana-Champaign, 2013. Google Scholar
  7. A. Ene, J. Vondrák, and Y. Wu. Local distribution and the symmetry gap: Approximability of multiway partitioning problems. In Proc. of ACM-SIAM SODA, pages 306-325, 2013. Google Scholar
  8. S. Fujishige. Submodular functions and optimization. Elsevier Science, 2005. Google Scholar
  9. M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, 1979. Google Scholar
  10. N. Garg, V. V. Vazirani, and M. Yannakakis. Multiway cuts in node weighted graphs. Journal of Algorithms, 50(1):49-61, 2004. Google Scholar
  11. D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An approach based on dynamical systems. VLDB Journal, 8(3-4):222-236, 2000. Google Scholar
  12. G. Goel, C. Karande, P. Tripathi, and L. Wang. Approximability of combinatorial problems with multi-agent submodular cost functions. In Proc. of IEEE FOCS, pages 755-764, 2009. Google Scholar
  13. S. Iwata, L. Fleischer, and S. Fujishige. A combinatorial, strongly polynomial-time algorithm for minimizing submodular functions. In Proc. of ACM STOC, pages 97-106, 2000. Google Scholar
  14. S. Iwata and K. Nagano. Submodular function minimization under covering constraints. In Proc. of IEEE FOCS, pages 671-680, 2009. Google Scholar
  15. S. Iwata and J. B. Orlin. A simple combinatorial algorithm for submodular function minimization. In Proc. of ACM-SIAM SODA, pages 1230-1237, 2009. Google Scholar
  16. J. M. Kleinberg and E. Tardos. Approximation algorithms for classification problems with pairwise relationships: Metric labeling and Markov random fields. Journal of the ACM, 49(5):616-639, 2002. Google Scholar
  17. M. Mirzakhani, 2014. personal communication. Google Scholar
  18. K. Okumoto, T. Fukunaga, and H. Nagamochi. Divide-and-conquer algorithms for partitioning hypergraphs and submodular systems. Algorithmica, pages 1-20, 2010. Google Scholar
  19. M. Queyranne. A combinatorial algorithm for minimizing symmetric submodular functions. In Proc. of ACM-SIAM SODA, pages 98-101, 1995. Google Scholar
  20. T. J. Schaefer. The complexity of satisfiability problems. In Proc. of ACM STOC, pages 216-226, 1978. Google Scholar
  21. A. Schrijver. A combinatorial algorithm minimizing submodular functions in strongly polynomial time. Journal of Combinatorial Theory, Series B, 80(2):346-355, 2000. Google Scholar
  22. E. Sperner. Neuer Beweis für die Invarianz der Dimensionszahl und des Gebietes. Math. Sem. Univ. Hamburg, 6:265-272, 1928. Google Scholar
  23. Z. Svitkina and L. Fleischer. Submodular approximation: Sampling-based algorithms and lower bounds. In Proc. of IEEE FOCS, pages 697-706, 2008. Google Scholar
  24. Z. Svitkina and E. Tardos. Facility location with hierarchical facility costs. ACM Transactions on Algorithms, 6(2):1-22, 2010. Google Scholar
  25. L. Zhao, H. Nagamochi, and T. Ibaraki. Greedy splitting algorithms for approximating multiway partition problems. Mathematical Programming, 102(1):167-183, 2005. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail